SevenNet-Omni-i12

Version: v0.12.0 Added: 2026-01-12 Published: 2026-01-12 54.9M parameters Missing preds: 0 pip install sevenn
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Predictions
κSRMEGeometry OptimizationDiscovery

Convex hull distance prediction errors projected onto elements

1 H 0.13
2 He 0.00
3 Li 0.02
4 Be 0.02
5 B 0.05
6 C 0.05
7 N 0.07
8 O 0.11
9 F 0.10
10 Ne 0.00
11 Na 0.02
12 Mg 0.03
13 Al 0.04
14 Si 0.05
15 P 0.05
16 S 0.06
17 Cl 0.08
18 Ar 0.00
19 K 0.03
20 Ca 0.03
21 Sc 0.03
22 Ti 0.04
23 V 0.06
24 Cr 0.08
25 Mn 0.11
26 Fe 0.09
27 Co 0.04
28 Ni 0.04
29 Cu 0.03
30 Zn 0.03
31 Ga 0.04
32 Ge 0.05
33 As 0.05
34 Se 0.08
35 Br 0.07
36 Kr 0.00
37 Rb 0.03
38 Sr 0.03
39 Y 0.04
40 Zr 0.04
41 Nb 0.05
42 Mo 0.05
43 Tc 0.03
44 Ru 0.05
45 Rh 0.04
46 Pd 0.04
47 Ag 0.03
48 Cd 0.03
49 In 0.05
50 Sn 0.04
51 Sb 0.05
52 Te 0.11
53 I 0.05
54 Xe 0.00
55 Cs 0.03
56 Ba 0.03
57 La 0.03
58 Ce 0.03
59 Pr 0.03
60 Nd 0.03
61 Pm 0.03
62 Sm 0.03
63 Eu 0.06
64 Gd 0.04
65 Tb 0.03
66 Dy 0.03
67 Ho 0.03
68 Er 0.03
69 Tm 0.03
70 Yb 0.04
71 Lu 0.03
72 Hf 0.04
73 Ta 0.07
74 W 0.04
75 Re 0.04
76 Os 0.05
77 Ir 0.06
78 Pt 0.05
79 Au 0.05
80 Hg 0.03
81 Tl 0.03
82 Pb 0.05
83 Bi 0.04
84 Po 0.00
85 At 0.00
86 Rn 0.00
87 Fr 0.00
88 Ra 0.00
89 Ac 0.03
90 Th 0.04
91 Pa 0.04
92 U 0.05
93 Np 0.09
94 Pu 0.20
95 Am 0.00
96 Cm 0.00
97 Bk 0.00
98 Cf 0.00
99 Es 0.00
100 Fm 0.00
101 Md 0.00
102 No 0.00
103 Lr 0.00
104 Rf 0.00
105 Db 0.00
106 Sg 0.00
107 Bh 0.00
108 Hs 0.00
109 Mt 0.00
110 Ds 0.00
111 Rg 0.00
112 Cn 0.00
113 Nh 0.00
114 Fl 0.00
115 Mc 0.00
116 Lv 0.00
117 Ts 0.00
118 Og 0.00
57-71 La-Lu Lanthanides
89-103 Ac-Lr Actinides

Model Authors

  1. Jaesun Kim  Seoul National University      
  2. Jinmu You  Seoul National University      
  3. Yutack Park  Seoul National University      
  4. Suyeon Ju  Seoul National University      
  5. Haekwan Jeon  Seoul National University      
  6. Seungwu Han  Seoul National University, Korea Institute for Advanced Study    

Trained By

  1. Jinmu You (Seoul National University)

Model Info

  • Model Version v0.12.0
  • Model Type UIP
  • Targets EFSG
  • Openness OSOD
  • Train Task S2EFS
  • Test Task IS2RE-SR
  • Trained for Benchmark No

Training Set

COSMOSDataset: 243M structures

Description

SevenNet is a graph neural network interatomic potential package that supports parallel molecular dynamics simulations. The SevenNet-Omni model employs a multi-task training strategy that jointly optimizes universal and task-specific parameters via selective regularization and domain-bridging strategies, enabling robust transferability across molecules, bulk crystals, and surfaces. Trained on 15 open datasets spanning molecular, inorganic, and interfacial chemistries, SevenNet-Omni achieves state-of-the-art cross-domain accuracy while maintaining high in-domain fidelity.

Hyperparameters

  • max_force: 0.02
  • max_steps: 800
  • ase_optimizer: "FIRE"
  • cell_filter: "FrechetCellFilter"
  • optimizer: "Adam"
  • loss: "MAE/L2MAE/L2MAE"
  • loss_weights: {"energy":1,"force":1,"stress":0.0005}
  • batch_size: 256
  • initial_learning_rate: 0.0001
  • learning_rate_schedule: "onecyclelr - max_lr=0.0001, pct_start=0.05, anneal_strategy=cos, div_factor=25, final_div_factor=1e4"
  • epochs: 2
  • n_layers: 12
  • n_features: ["128x0e","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e"]
  • n_radial_bessel_basis: 8
  • graph_construction_radius: 6
  • max_neighbors: null
  • sph_harmonics_l_max: 3

Dependencies